Serum potassium and outcomes in heart failure with preserved ejection fraction: a post‐hoc analysis of the <scp>PARAGON‐HF</scp> trial
Bibliographic record
Abstract
AIMS: The relationship between serum potassium concentration and outcomes in patients with heart failure and preserved ejection fraction (HFpEF) is not well-established. The aim of this study was to explore the association between serum potassium and clinical outcomes in the PARAGON-HF trial in which 4822 patients with HFpEF were randomised to treatment with sacubitril/valsartan or valsartan. METHODS AND RESULTS: The relationship between serum potassium concentrations and the primary study composite outcome of total (first and recurrent) heart failure hospitalisations and cardiovascular death was analysed. Hypo-, normo-, and hyperkalaemia were defined as serum potassium <4 mmol/L, 4-5 mmol/L and >5 mmol/L, respectively. Both screening and time-updated potassium (categorical and continuous spline-transformed) were studied. Patient mean age was 73 years and 52% were women. Patients with higher baseline potassium more often had an ischaemic aetiology and diabetes and mineralocorticoid receptor antagonist treatment. Compared with normokalaemia, both time-updated (but not screening) hypo- and hyperkalaemia were associated with a higher risk of the primary outcome [adjusted hazard ratio (HR) for hypokalaemia 1.55, 95% confidence interval (CI) 1.30-1.85; P < 0.001, and for hyperkalaemia HR 1.21, 95% CI 1.02-1.44; P = 0.025]. Hypokalaemia had a stronger association with a higher risk of all-cause, cardiovascular and non-cardiovascular death than hyperkalaemia. The association of hypokalaemia with increased risk of all-cause and cardiovascular death was most marked in participants with impaired kidney function (interaction P < 0.05). Serum potassium did not significantly differ between sacubitril/valsartan and valsartan throughout the follow-up. CONCLUSIONS: Both hypo- and hyperkalaemia were associated with heart failure hospitalisation but only hypokalaemia was associated with mortality, especially in the context of renal impairment. Hypokalaemia was as strongly associated with death from non-cardiovascular causes as with cardiovascular death. Collectively, these findings suggest that potassium disturbances are a more of a marker of HFpEF severity rather than a direct cause of death.
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How this classification was reachedexpand
Full frame machine prediction
Teacher imitationNot calibrated prevalence, not ground truth. Human validation pending. The Gemma side is a direct model label for every work in the frame, read from the title-only record. The Codex side is a classifier learned from the 10,348 direct Codex labels and calibrated to design-weighted sample rates; fields without enough sample support carry no Codex call. Candidate is the union of the two sides; consensus is their intersection. These outputs are machine_predicted_unvalidated and are not human labels.
Distilled classifier scores by category (both heads)
| Category | Codex | Gemma |
|---|---|---|
| Metaresearch | 0.004 | 0.004 |
| Meta-epidemiology (narrow) | 0.001 | 0.000 |
| Meta-epidemiology (broad) | 0.004 | 0.004 |
| Bibliometrics | 0.000 | 0.000 |
| Science and technology studies | 0.000 | 0.000 |
| Scholarly communication | 0.001 | 0.001 |
| Open science | 0.001 | 0.001 |
| Research integrity | 0.001 | 0.002 |
| Insufficient payload (model declined to judge) | 0.003 | 0.000 |
Machine scores (provisional)
The two teacher heads of the student model, read on this work. A score orders the frame for review; it never asserts a category, and the validation status ships verbatim with every row.
Baseline scores from an immature model (maturity gate not passed, 7 training rounds). Scores rank; they never assert a category.
score_only:v0-immature-baseline · verbatim from the scoring run: score_only means the number may rank works, and no category label ships from itClassification
machine, unvalidatedMachine predicted; a candidate call from one source (direct Gemma or distilled Codex), not a consensus.
How this classification was reached, model by model and score by score, is at the end of the page under "How this classification was reached".